Structured sparse representations for supervised and unsupervised learning
Many problems in machine learning (ML) and computer vision (CV) deal with large amounts of data with variations and noise for underlying tasks. For example, object detection requires filtering semantic objects from noisy and varying backgrounds, and the ImageNet challenge requires to build a classif...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/143420 |